Adaptive noise cancellation by LMS algorithm

The research on controlling the noise level in an environment has been the focus of many researchers over the last few years. Adaptive noise cancellation (ANC) is one such approach that has been proposed for reduction of steady state noise. In this research, the least mean square (LMS) algorithm...

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Main Author:	Saon, Sharifah
Format:	Thesis
Language:	English English English
Published:	2004
Subjects:	TD Environmental technology Sanitary engineering TD Environmental technology. Sanitary engineering
Online Access:	http://eprints.uthm.edu.my/8631/1/24p%20SHARIFAH%20SAON.pdf http://eprints.uthm.edu.my/8631/2/SHARIFAH%20SAON%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/8631/3/SHARIFAH%20SAON%20WATERMARK.pdf
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id	my-uthm-ep.8631
record_format	uketd_dc
spelling	my-uthm-ep.86312023-05-02T02:11:57Z Adaptive noise cancellation by LMS algorithm 2004-04 Saon, Sharifah TD Environmental technology. Sanitary engineering TD878-894 Special types of environment, Including soil pollution, air pollution, noise pollution The research on controlling the noise level in an environment has been the focus of many researchers over the last few years. Adaptive noise cancellation (ANC) is one such approach that has been proposed for reduction of steady state noise. In this research, the least mean square (LMS) algorithm using MATLAB was implemented. Step size determination was done to determine the best step size and effects of the rate of convergence. Sound recorder was used to record sound and saved as .wav file. Graphical user interface (GUI) was created to make it user friendly. The output of the analysis showed that the best step size was 0.008. Smaller step size of 0.001 tend to lower the speed of convergence, and too big a step size, 0.8 tend to cause the system to diverge. Analysis on synthesized data showed that the noise reduction did not eliminate the original signal. The implementation on actual data showed slight difference between the output and input level. In real situation, as in theory, this technique can be used to reduce noise level from noisy signal without reducing the characteristic of the signal. 2004-04 Thesis http://eprints.uthm.edu.my/8631/ http://eprints.uthm.edu.my/8631/1/24p%20SHARIFAH%20SAON.pdf text en public http://eprints.uthm.edu.my/8631/2/SHARIFAH%20SAON%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/8631/3/SHARIFAH%20SAON%20WATERMARK.pdf text en validuser mphil masters Kolej Universiti Teknologi Tun Hussein Onn Fakulti Teknologi Kejuruteraan
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	TD Environmental technology Sanitary engineering TD Environmental technology. Sanitary engineering
spellingShingle	TD Environmental technology Sanitary engineering TD Environmental technology. Sanitary engineering Saon, Sharifah Adaptive noise cancellation by LMS algorithm
description	The research on controlling the noise level in an environment has been the focus of many researchers over the last few years. Adaptive noise cancellation (ANC) is one such approach that has been proposed for reduction of steady state noise. In this research, the least mean square (LMS) algorithm using MATLAB was implemented. Step size determination was done to determine the best step size and effects of the rate of convergence. Sound recorder was used to record sound and saved as .wav file. Graphical user interface (GUI) was created to make it user friendly. The output of the analysis showed that the best step size was 0.008. Smaller step size of 0.001 tend to lower the speed of convergence, and too big a step size, 0.8 tend to cause the system to diverge. Analysis on synthesized data showed that the noise reduction did not eliminate the original signal. The implementation on actual data showed slight difference between the output and input level. In real situation, as in theory, this technique can be used to reduce noise level from noisy signal without reducing the characteristic of the signal.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Saon, Sharifah
author_facet	Saon, Sharifah
author_sort	Saon, Sharifah
title	Adaptive noise cancellation by LMS algorithm
title_short	Adaptive noise cancellation by LMS algorithm
title_full	Adaptive noise cancellation by LMS algorithm
title_fullStr	Adaptive noise cancellation by LMS algorithm
title_full_unstemmed	Adaptive noise cancellation by LMS algorithm
title_sort	adaptive noise cancellation by lms algorithm
granting_institution	Kolej Universiti Teknologi Tun Hussein Onn
granting_department	Fakulti Teknologi Kejuruteraan
publishDate	2004
url	http://eprints.uthm.edu.my/8631/1/24p%20SHARIFAH%20SAON.pdf http://eprints.uthm.edu.my/8631/2/SHARIFAH%20SAON%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/8631/3/SHARIFAH%20SAON%20WATERMARK.pdf
_version_	1776103376768991232

Adaptive noise cancellation by LMS algorithm

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